AoI-Driven Drone-Assisted Crowdsensing in Social IoT: A Deep Reinforcement Learning Approach
Zheng Zhang, Jingjing Wang, Jianrui Chen, Ziyang Wang, Peng Pan · IEEE Internet of Things Journal · 2025
The extensive deployment of Internet of Things (IoT) devices across diverse industries has introduced substantial challenges in information collection, which exactly hinders its further advancement. By virtue of its flexibility and mobility, mobile crowdsensing (MCS), particularly drone-assisted mobile crowdsensing, is regarded as a new paradigm for addressing information collection problems in IoT environments. Nonetheless, owing to the inherent size and energy constraints of drones, planning their trajectories to efficiently perform the crowdsensing tasks from a large number of heterogeneous and spatiotemporally distributed IoT devices presents a significant problem. In this paper, we develop a multi-drone assisted crowdsensing social IoT (SIoT) system that integrates the social attributes of IoT devices and enables performing social community-oriented crowdsensing. Given the significance of information freshness in crowdsensing tasks, we jointly optimize the age of information (AoI) and drone energy consumption within the problem of multi-drone trajectory planning. We formulate the aforementioned problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose a deep-reinforcement-learning-based algorithm to address this problem. A series of experiments is conducted and the simulation results demonstrate the superiority and robustness of the proposed algorithm in effectively balancing the AoI of the whole SIoT system and energy consumption of drones during the crowdsensing tasks.